def make_and_save_dataset(patch_len, name, leads_names):
json_data = select_and_load_json()
numpy_data = get_numpy_from_json(json_data, patch_len, leads_names)
file_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".json"
np_to_json(numpy_data, file_path)
print("np dataset saved to " + str(file_path))
print("shape: " + str(numpy_data.shape))
def save_np_u_selectors(cutted_signals_batch, contexts_batch, name):
signal_patches_filename = PATH_TO_SELECTORS + name + ".sig"
measurs_filename = PATH_TO_SELECTORS + name + ".conext"
np_to_json(np.array(cutted_signals_batch), signal_patches_filename)
np_to_json(np.array(contexts_batch), measurs_filename)
print("Saved signal: " + signal_patches_filename)
print("Saved context: " + measurs_filename)
def save_sample(np_validities, np_measuremens, name):
path = PATH_TO_SAMPLES_FROM_MODELS + name
val_name = path + ".valid"
meas_name = path + ".measur"
np_to_json(np_validities, val_name)
np_to_json(np_measuremens, meas_name)
print('Saved: ' + val_name + ", of shape "+ str(np_validities.shape))
print('Saved: ' + meas_name + ", of shape "+ str(np_validities.shape))
def downsample_and_save_np_dataset(name, np_arr=None):
if np_arr is None:
np_arr = select_and_load_np_data()
downsampled_np = downsample_dataset(np_arr)
path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".np"
np_to_json(downsampled_np, path)
print("np dataset saved to " + str(path))
print("shape: " + str(downsampled_np.shape))
def make_and_save_random_dataset(patch_len, name, leads_names,
amount_of_patches):
json_data = select_and_load_json()
numpy_data, numpy_labels = get_numpy_from_json(json_data, patch_len,
leads_names,
amount_of_patches)
signal_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".np"
np_to_json(numpy_data, signal_path)
label_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".sig"
np_to_json(numpy_labels, label_path)
print("np dataset saved to " + str(signal_path))
print("shape: " + str(numpy_data.shape))
def show_and_save_output(batch_size, name, gen_model=None):
if gen_model is None:
gen_model = restore_model_from_file()
z, label_input_one_hot, code_input, label_input_int\
= sample_input_for_generator(gen_model, batch_size)
gen_model.cuda()
out = gen_model(z, label_input_one_hot, code_input)
np_out = out.cpu().detach().numpy()
show_np_dataset_1st_lead(np_out)
#---------save it as dataset------
file_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".json"
np_to_json(np_out, file_path)
print("np dataset saved to " + str(file_path))
def experiment(patch_len, dispersion):
leads_names = ['i']
from settings import PATH_TO_METADATASETS_FOLDER
name = str(patch_len) + "_t_i_normal_cs_stdepr" + str(int(dispersion))
path1 = PATH_TO_METADATASETS_FOLDER + "\\7_pacients_ideally_healthy_and_normal_axis.json"
json_data1 = select_and_load_json(path1)
numpy_data1, labels1 = get_numpy_from_json(json_data1, patch_len,
leads_names)
meta_label1 = np.full(labels1.shape, 0)
path2 = PATH_TO_METADATASETS_FOLDER + "\\st_depression6.json"
json_data2 = select_and_load_json(path2)
numpy_data2, labels2 = get_numpy_from_json(json_data2, patch_len,
leads_names)
meta_label2 = np.full(labels2.shape, 1)
numpy_data = np.concatenate((numpy_data1, numpy_data2))
shift_labels = np.concatenate((labels1, labels2))
meta_labels = np.concatenate((meta_label1, meta_label2))
numpy_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".np"
label_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".labels_shifts"
meta_labels_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".labels_meta"
np_to_json(numpy_data, numpy_path)
np_to_json(shift_labels, label_path)
np_to_json(meta_labels, meta_labels_path)
print("np dataset saved to " + str(numpy_path))
print("shape: " + str(numpy_data.shape))
def make_and_save_from2jsons(patch_len, name, leads_names):
json_data1 = select_and_load_json()
numpy_data1, labels1 = get_numpy_from_json(json_data1, patch_len,
leads_names)
meta_label1 = np.full(labels1.shape, 0)
json_data2 = select_and_load_json()
numpy_data2, labels2 = get_numpy_from_json(json_data2, patch_len,
leads_names)
meta_label2 = np.full(labels2.shape, 1)
numpy_data = np.concatenate((numpy_data1, numpy_data2))
shift_labels = np.concatenate((labels1, labels2))
meta_labels = np.concatenate((meta_label1, meta_label2))
numpy_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".np"
label_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".labels_shifts"
meta_labels_path = PATH_TO_NUMPY_DATA_FOLDER + "\\" + name + ".labels_meta"
np_to_json(numpy_data, numpy_path)
np_to_json(shift_labels, label_path)
np_to_json(meta_labels, meta_labels_path)
print("np dataset saved to " + str(numpy_path))
print("shape: " + str(numpy_data.shape))